For this ablation study, we made minor modifications to our model: i.e., output spatial resolution and embedded features. We refer the readers to either Appendix or github page: https://seokju-cho.github.io/VAT/ for details. Note that this is possible as our projection head also outputs the same dimension as if a flow field is inferred. Following common protocol [1]}, [2]}, [3]}, [4]}, we use standard benchmarks for this task, which we trained our model on training split of PF-PASCAL [5]} when evaluated on test split of PF-PASCAL [5]} and PF-WILLOW [7]}, and trained on SPair-71k [8]} when evaluated on SPair-71k [8]}. We fine-tuned the feature backbone for fair comparison. As shown in Table REF  and Fig. REF , although not specifically designed for semantic correspondence task, VAT either sets a new state-of-the-art [8]}, [7]} or attain the second highest PCK [5]} for this task. This results show that cost aggregation is a prime importance in both few-shot segmentation and semantic correspondence.
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